arXiv subjects

Jingwei Zhang

Publications and source records attributed to Jingwei Zhang.

81 records · Page 5Linked to original sources

arXiv2018

Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning

This paper investigates exploration strategies of Deep Reinforcement Learning (DRL) methods to learn navigation policies for mobile robots. In particular, we augment the normal external reward for training DRL algorithms with intrinsic reward signals measured by curiosity. We test our approach in a mapless navigation setting, where the autonomous agent is required to navigate without the occupancy map of the environment, to targets whose relative locations can be easily acquired through low-cost solutions (e.g., visible light localization, Wi-Fi signal localization). We validate that the intrinsic motivation is crucial for improving DRL performance in tasks with challenging exploration requirements. Our experimental results show that our proposed method is able to more effectively learn navigation policies, and has better generalization capabilities in previously unseen environments. A video of our experimental results can be found at https://goo.gl/pWbpcF.

cs.RO↗

arXiv2018

A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation

Deep learning techniques have been widely applied, achieving state-of-the-art results in various fields of study. This survey focuses on deep learning solutions that target learning control policies for robotics applications. We carry out our discussions on the two main paradigms for learning control with deep networks: deep reinforcement learning and imitation learning. For deep reinforcement learning (DRL), we begin from traditional reinforcement learning algorithms, showing how they are extended to the deep context and effective mechanisms that could be added on top of the DRL algorithms. We then introduce representative works that utilize DRL to solve navigation and manipulation tasks in robotics. We continue our discussion on methods addressing the challenge of the reality gap for transferring DRL policies trained in simulation to real-world scenarios, and summarize robotics simulation platforms for conducting DRL research. For imitation leaning, we go through its three main categories, behavior cloning, inverse reinforcement learning and generative adversarial imitation learning, by introducing their formulations and their corresponding robotics applications. Finally, we discuss the open challenges and research frontiers.

cs.RO↗

arXiv2018

Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning

We present an approach for mobile robots to learn to navigate in dynamic environments with pedestrians via raw depth inputs, in a socially compliant manner. To achieve this, we adopt a generative adversarial imitation learning (GAIL) strategy, which improves upon a pre-trained behavior cloning policy. Our approach overcomes the disadvantages of previous methods, as they heavily depend on the full knowledge of the location and velocity information of nearby pedestrians, which not only requires specific sensors, but also the extraction of such state information from raw sensory input could consume much computation time. In this paper, our proposed GAIL-based model performs directly on raw depth inputs and plans in real-time. Experiments show that our GAIL-based approach greatly improves the safety and efficiency of the behavior of mobile robots from pure behavior cloning. The real-world deployment also shows that our method is capable of guiding autonomous vehicles to navigate in a socially compliant manner directly through raw depth inputs. In addition, we release a simulation plugin for modeling pedestrian behaviors based on the social force model.

cs.RO↗

arXiv2017

Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments

In this paper we consider the problem of robot navigation in simple maze-like environments where the robot has to rely on its onboard sensors to perform the navigation task. In particular, we are interested in solutions to this problem that do not require localization, mapping or planning. Additionally, we require that our solution can quickly adapt to new situations (e.g., changing navigation goals and environments). To meet these criteria we frame this problem as a sequence of related reinforcement learning tasks. We propose a successor feature based deep reinforcement learning algorithm that can learn to transfer knowledge from previously mastered navigation tasks to new problem instances. Our algorithm substantially decreases the required learning time after the first task instance has been solved, which makes it easily adaptable to changing environments. We validate our method in both simulated and real robot experiments with a Robotino and compare it to a set of baseline methods including classical planning-based navigation.

cs.RO↗

arXiv2015

Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections

Weak topic correlation across document collections with different numbers of topics in individual collections presents challenges for existing cross-collection topic models. This paper introduces two probabilistic topic models, Correlated LDA (C-LDA) and Correlated HDP (C-HDP). These address problems that can arise when analyzing large, asymmetric, and potentially weakly-related collections. Topic correlations in weakly-related collections typically lie in the tail of the topic distribution, where they would be overlooked by models unable to fit large numbers of topics. To efficiently model this long tail for large-scale analysis, our models implement a parallel sampling algorithm based on the Metropolis-Hastings and alias methods (Yuan et al., 2015). The models are first evaluated on synthetic data, generated to simulate various collection-level asymmetries. We then present a case study of modeling over 300k documents in collections of sciences and humanities research from JSTOR.

cs.CL↗

arXiv2012

What is the Nature of Chinese MicroBlogging: Unveiling the Unique Features of Tencent Weibo

China has the largest number of online users in the world and about 20% internet users are from China. This is a huge, as well as a mysterious, market for IT industry due to various reasons such as culture difference. Twitter is the largest microblogging service in the world and Tencent Weibo is one of the largest microblogging services in China. Employ the two data sets as a source in our study, we try to unveil the unique behaviors of Chinese users. We have collected the entire Tencent Weibo from 10th, Oct, 2011 to 5th, Jan, 2012 and obtained 320 million user profiles, 5.15 billion user actions. We study Tencent Weibo from both macro and micro levels. From the macro level, Tencent users are more active on forwarding messages, but with less reciprocal relationships than Twitter users, their topic preferences are very different from Twitter users from both content and time consuming; besides, information can be diffused more efficient in Tencent Weibo. From the micro level, we mainly evaluate users' social influence from two indexes: "Forward" and \Follower", we study how users' actions will contribute to their social influences, and further identify unique features of Tencent users. According to our studies, Tencent users' actions are more personalized and diversity, and the influential users play a more important part in the whole networks. Based on the above analysis, we design a graphical model for predicting users' forwarding behaviors. Our experimental results on the large Tencent Weibo data validate the correctness of the discoveries and the effectiveness of the proposed model. To the best of our knowledge, this work is the first quantitative study on the entire Tencentsphere and information diffusion on it.

cs.SI↗

arXiv2012

User-level Weibo Recommendation incorporating Social Influence based on Semi-Supervised Algorithm

Tencent Weibo, as one of the most popular micro-blogging services in China, has attracted millions of users, producing 30-60 millions of weibo (similar as tweet in Twitter) daily. With the overload problem of user generate content, Tencent users find it is more and more hard to browse and find valuable information at the first time. In this paper, we propose a Factor Graph based weibo recommendation algorithm TSI-WR (Topic-Level Social Influence based Weibo Recommendation), which could help Tencent users to find most suitable information. The main innovation is that we consider both direct and indirect social influence from topic level based on social balance theory. The main advantages of adopting this strategy are that it could first build a more accurate description of latent relationship between two users with weak connections, which could help to solve the data sparsity problem; second provide a more accurate recommendation for a certain user from a wider range. Other meaningful contextual information is also combined into our model, which include: Users profile, Users influence, Content of weibos, Topic information of weibos and etc. We also design a semi-supervised algorithm to further reduce the influence of data sparisty. The experiments show that all the selected variables are important and the proposed model outperforms several baseline methods.

cs.SI↗

arXiv2012

Topic-Level Opinion Influence Model(TOIM): An Investigation Using Tencent Micro-Blogging

Mining user opinion from Micro-Blogging has been extensively studied on the most popular social networking sites such as Twitter and Facebook in the U.S., but few studies have been done on Micro-Blogging websites in other countries (e.g. China). In this paper, we analyze the social opinion influence on Tencent, one of the largest Micro-Blogging websites in China, endeavoring to unveil the behavior patterns of Chinese Micro-Blogging users. This paper proposes a Topic-Level Opinion Influence Model (TOIM) that simultaneously incorporates topic factor and social direct influence in a unified probabilistic framework. Based on TOIM, two topic level opinion influence propagation and aggregation algorithms are developed to consider the indirect influence: CP (Conservative Propagation) and NCP (None Conservative Propagation). Users' historical social interaction records are leveraged by TOIM to construct their progressive opinions and neighbors' opinion influence through a statistical learning process, which can be further utilized to predict users' future opinions on some specific topics. To evaluate and test this proposed model, an experiment was designed and a sub-dataset from Tencent Micro-Blogging was used. The experimental results show that TOIM outperforms baseline methods on predicting users' opinion. The applications of CP and NCP have no significant differences and could significantly improve recall and F1-measure of TOIM.

cs.SI↗

arXiv2010

Effect of annealing temperature on morphology, structure and photocatalytic behavior of nanotubed H2Ti2O4(OH)2

Nanotubed titanic acid (H2Ti2O4(OH)2) was prepared from nanotubed sodium titanate (Na2Ti2O4(OH)2) by an ion exchange reaction in a pH=1 HCl solution. The effect of annealing temperature on the morphology, structure and photocatalytic behavior of nanotubed H2Ti2O4(OH)2 was studied by means of TEM, XRD, DTG, DSC, BET and ESR. The results showed that nanotubed H2Ti2O4(OH)2 is thermally unstable. Its dehydration consists of two steps. In the first-step dehydration, single-electron-trapped oxygen vacancies (SETOVs) were generated. Accompanying the second-step dehydration, the transition of crystal form from orthorhombic system to anatase took place, at the same time the nanotubes broke. At T>300 °C, when the SETOV concentration greatly increased, the interaction between SETOV happened. (VOo)x formed could play the role of recombination center of photogenerated e--h+ and make the photocatalytic behavior of TiO2 (anatase, obtained from 500 °C-treated nanotubed H2Ti2O4(OH)2) to become bad.

cond-mat.mtrl-sci↗